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Does Forecast‐Accuracy‐Based Allocation Induce Customers to Share Truthful Order Forecasts?

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  • Pelin Pekgün
  • Minseok Park
  • Pınar Keskinocak
  • Mani Janakiram

Abstract

Through a behavioral study, we investigate buyers’ strategic order forecasting behavior in a multi‐period setting under a forecast accuracy‐based allocation policy, where the supplier allocates (proportionally) more inventory to the buyer with the better order forecast accuracy in case of scarce supply. We developed an interactive game that simulates a supply chain in which one supplier sells a key component to two buyers, who in turn sell to consumers. In each period, buyers share forecasts of future orders with their supplier. The participants in the game play the role of a buyer, while the supplier is automated. Our experimental findings suggest that rewarding forecast accuracy in allocating inventory can significantly improve the order forecast accuracy of the buyers and reduce their forecast inflation behavior. Interestingly, even without communication of the policy, buyers learn over time that more accurate forecasts lead to better service from their supplier and improve their order forecast accuracy.

Suggested Citation

  • Pelin Pekgün & Minseok Park & Pınar Keskinocak & Mani Janakiram, 2019. "Does Forecast‐Accuracy‐Based Allocation Induce Customers to Share Truthful Order Forecasts?," Production and Operations Management, Production and Operations Management Society, vol. 28(10), pages 2500-2513, October.
    • Handle: RePEc:bla:popmgt:v:28:y:2019:i:10:p:2500-2513
    DOI: 10.1111/poms.13066
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    Cited by:

    1. Eirini Spiliotopoulou & Karen Donohue & Mustafa Çagri Gürbüz, 2022. "Ordering Behavior and the Impact of Allocation Mechanisms in an Integrated Distribution System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 422-441, February.
    2. Tarikere T. Niranjan & Narendra K. Ghosalya & Srinagesh Gavirneni, 2022. "Crying Wolf and a Knowing Wink: A Behavioral Study of Order Inflation and Discounting in Supply Chains," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1071-1088, March.
    3. Baixun Li & Meng Li & Chao Liang, 2023. "Cry‐wolf syndrome in recommendation," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 347-358, February.
    4. Villa, Sebastián, 2022. "Competing for supply and demand: Understanding retailers' ordering decisions," International Journal of Production Economics, Elsevier, vol. 244(C).
    5. Ping Yan & Jun Pei & Ya Zhou & Panos M. Pardalos, 2024. "When platform exploits data analysis advantage: change of OEM-led supply chain structure," Annals of Operations Research, Springer, vol. 339(3), pages 1405-1431, August.

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